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Edge Computing in Internet of Things Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 5158

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Special Issue Editors

Dr. Junwei Yan

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Guest Editor

Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan 430070, China
Interests: industrial Internet of Things; digital twin; fiber grating sensor; intelligent monitoring; thermal error compensation; edge intelligence

Dr. Alberto Gotta

E-Mail Website
Guest Editor

Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), 56124 Pisa, Italy
Interests: wireless networks; satellite communication; wireless communications; Internet of Things; artificial intelligence; neural networks
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Special Issue Information

Dear Colleagues,

The Internet of Things has been widely used in many fields such as industry, transportation, health, and security. As an emerging information technology, edge computing can effectively improve the intelligence level of the Internet of Things, promoting the digitization, networking and intelligence of sensors and network devices. The integration of edge computing and the Internet of Things will expand the intelligent application of information technology in a wide variety of fields.

Dr. Junwei Yan
Dr. Alberto Gotta
Guest Editors

Manuscript Submission Information

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Keywords

edge computing
Internet of Things
edge intelligence
multi-sensor integration
intelligent information processing
situation awareness

Published Papers (4 papers)

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Research

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27 pages, 2337 KiB

Open AccessArticle

An Edge Computing Application of Fundamental Frequency Extraction for Ocean Currents and Waves

by Nieves G. Hernandez-Gonzalez, Juan Montiel-Caminos, Javier Sosa and Juan A. Montiel-Nelson

Sensors 2024, 24(5), 1358; https://doi.org/10.3390/s24051358 - 20 Feb 2024

Viewed by 469

Abstract

This paper describes the design and optimization of a smart algorithm based on artificial intelligence to increase the accuracy of an ocean water current meter. The main purpose of water current meters is to obtain the fundamental frequency of the ocean waves and currents. The limiting factor in those underwater applications is power consumption and that is the reason to use only ultra-low power microcontrollers. On the other hand, nowadays extraction algorithms assume that the processed signal is defined in a fixed bandwidth. In our approach, belonging to the edge computing research area, we use a deep neural network to determine the narrow bandwidth for filtering the fundamental frequency of the ocean waves and currents on board instruments. The proposed solution is implemented on an 8 MHz ARM Cortex-M0+ microcontroller without a floating point unit requiring only 9.54 ms in the worst case based on a deep neural network solution. Compared to a greedy algorithm in terms of computational effort, our worst-case approach is 1.81 times faster than a fast Fourier transform with a length of 32 samples. The proposed solution is 2.33 times better when an artificial neural network approach is adopted. Full article

(This article belongs to the Special Issue Edge Computing in Internet of Things Applications)

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27 pages, 22777 KiB

Open AccessArticle

The Lightweight Anchor Dynamic Assignment Algorithm for Object Detection

by Ping Han, Xujun Zhuang, Huahong Zuo, Ping Lou and Xiao Chen

Sensors 2023, 23(14), 6306; https://doi.org/10.3390/s23146306 - 11 Jul 2023

Viewed by 950

Abstract

Smart security based on object detection is one of the important applications of edge computing in IoT. Anchors in object detection refer to points on the feature map, which can be used to generate anchor boxes and serve as training samples. Current object detection models do not consider the aspect ratio of the ground-truth boxes in anchor assignment and are not well-adapted to objects with very different shapes. Therefore, this paper proposes the Lightweight Anchor Dynamic Assignment algorithm (LADA) for object detection. LADA does not change the structure of the original detection model; first, it selects an equal proportional center region based on the aspect ratio of the ground-truth box, then calculates the combined loss of anchors, and finally divides the positive and negative samples more efficiently by dynamic loss threshold without additional models. The algorithm solves the problems of poor adaptability and difficulty in the selection of the best positive samples based on IoU assignment, and the sample assignment for eccentric objects and objects with different aspect ratios was more reasonable. Compared with existing sample assignment algorithms, the LADA algorithm outperforms the MS COCO dataset by 1.66% over the AP of the baseline FCOS, and 0.76% and 0.24% over the AP of the ATSS algorithm and the PAA algorithm, respectively, with the same model structure, which demonstrates the effectiveness of the LADA algorithm. Full article

(This article belongs to the Special Issue Edge Computing in Internet of Things Applications)

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27 pages, 1717 KiB

Open AccessArticle

Energy Efficient Node Selection in Edge-Fog-Cloud Layered IoT Architecture

by Rolden Fereira, Chathurika Ranaweera, Kevin Lee and Jean-Guy Schneider

Sensors 2023, 23(13), 6039; https://doi.org/10.3390/s23136039 - 29 Jun 2023

Cited by 3 | Viewed by 1473

Abstract

Internet of Things (IoT) architectures generally focus on providing consistent performance and reliable communications. The convergence of IoT, edge, fog, and cloud aims to improve the quality of service of applications, which does not typically emphasize energy efficiency. Considering energy in IoT architectures would reduce the energy impact from billions of IoT devices. The research presented in this paper proposes an optimization framework that considers energy consumption of nodes when selecting a node for processing an IoT request in edge-fog-cloud layered architecture. The IoT use cases considered in this paper include smart grid, autonomous vehicles, and eHealth. The proposed framework is evaluated using CPLEX simulations. The results provide insights into mechanisms that can be used to select nodes energy-efficiently whilst meeting the application requirements and other network constraints in multi-layered IoT architectures. Full article

(This article belongs to the Special Issue Edge Computing in Internet of Things Applications)

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Review

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29 pages, 2381 KiB

Open AccessReview

Multi-Access Edge Computing (MEC) Based on MIMO: A Survey

by Mengyu Zhu, Shaoshuai Gao, Guofang Tu and Deyuan Chen

Sensors 2023, 23(8), 3883; https://doi.org/10.3390/s23083883 - 11 Apr 2023

Cited by 2 | Viewed by 1747

Abstract

With the rapid development of wireless communication technology and the emergence of intelligent applications, higher requirements have been put forward for data communication and computing capacity. Multi-access edge computing (MEC) can handle highly demanding applications by users by sinking the services and computing capabilities of the cloud to the edge of the cell. Meanwhile, the multiple input multiple output (MIMO) technology based on large-scale antenna arrays can achieve an order-of-magnitude improvement in system capacity. The introduction of MIMO into MEC takes full advantage of the energy and spectral efficiency of MIMO technology, providing a new computing paradigm for time-sensitive applications. In parallel, it can accommodate more users and cope with the inevitable trend of continuous data traffic explosion. In this paper, the state-of-the-art research status in this field is investigated, summarized and analyzed. Specifically, we first summarize a multi-base station cooperative mMIMO-MEC model that can easily be expanded to adapt to different MIMO-MEC application scenarios. Subsequently, we comprehensively analyze the current works, compare them to each other and summarize them, mainly from four aspects: research scenarios, application scenarios, evaluation indicators and research issues, and research algorithms. Finally, some open research challenges are identified and discussed, and these indicate the direction for future research on MIMO-MEC. Full article

(This article belongs to the Special Issue Edge Computing in Internet of Things Applications)

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